Agilent Single-Channel Microarray Processing Pipeline RMarkdown (GLDS-174)

Installing necessary packages

For processing Agilent microarray data, I needed to install a few packages that were used for the importing and analyzing the data. This included Bioconductor as well as Linear Models for Microarray (Limma). These are popular packages for processing microarray data.

## For installing Bioconductor on R
# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install(version = "3.14")

## For installing limma on R
# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("limma")

Load the packages

After installing the packages above, I had to load them in using the library function so that I could access their functions.

library(limma)
library(dplyr)
library(statmod)

Importing the Raw Data

Now that the library was loaded, I was able to import the raw data. I started by importing the runsheet into R. This contained the necessary data for processing later in the pipeline.

dir = "C:/Users/tangk/Desktop/Agilent_Microarray"
GLDS_174_rs <- read.csv(file.path(dir, "Runsheet(GLDS-174).csv"), check.names = FALSE, fileEncoding = 'UTF-8-BOM') ## Outputs a dataframe
Factor_Value <- GLDS_174_rs[,c("Factor Value[sex]", "Factor Value[exposure duration]")]

• dir = A variable that stores the location of where I kept the runsheets.
• GLDS_174_rs = A varaible that holds the contents of those runsheets.
• Factor_Value = A variable that holds specifically the factor values column from the runsheet.

Here, I imported the raw data from the text files into R using the readmaimages() function from the Limma package.

datadir = "C:/Users/tangk/Desktop/Agilent_Microarray/input_data"
files = dir(path = file.path(datadir, "GLDS-174/GLDS-174_Raw_Data_Combined"), pattern="*\\.txt$")

## Input: ".txt" files, Output: EListRaw (Raw expression levels)
raw_data <- limma::read.maimages(files, source="agilent", path = file.path(datadir, "GLDS-174/GLDS-174_Raw_Data_Combined"), sep="\t", green.only = TRUE)

## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930654_1p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930655_1p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930656_1p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930657_1p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930658_1p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930659_1p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930660_3p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930661_3p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930662_3p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930663_3p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930664_3p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930665_3p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930666_4p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930667_4p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930668_4p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930669_4p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930670_4p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930671_4p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930672_6p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930673_6p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930674_6p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930675_6p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930676_6p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930677_6p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930678_7p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930679_7p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930680_7p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930681_7p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930682_7p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930683_7p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930684_8p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930685_8p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930686_8p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930687_8p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930688_8p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930689_8p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930690_9p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930691_9p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930692_9p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930693_9p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930694_9p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930695_9p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930696_10p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930697_10p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930698_10p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930699_10p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930700_10p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930701_10p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930702_11p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930703_11p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930704_11p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930705_11p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930706_11p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930707_11p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930708_12p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930709_12p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930710_12p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930711_12p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930712_12p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930713_12p6.txt

• datadir = A variable that stores the path to the directory that holds the three datasets that I have chosen.
• files = A variable that will detect all of the text files within the GLDS-174_Raw_Data_Combined directory. This is where all of the raw Agilent microarray data is stored for the GLDS-174 dataset.
• source = “agilent” because the data that I want to import will be agilent microarray data. There are other options for this flag for other types of microarrays
• path = The path to the directory that holds the dataset’s raw data.
• sep = How the data is separated in those raw data files (in our case it’s tab separated).
• green.only = TRUE, This command is for telling the function that it should only read the green (Cys3) channel rather than red and green since we’re only looking at single channel microarrays.

QC of the Raw Data

In this step, I looked at the raw data to make sure that it is in the correct format so that it could be processed correctly down the pipeline.

# This code block uses the plotDensities() function from limma to generate a graph with the raw intensities vs. density 
plotDensities(raw_data, log = TRUE, legend = "topright" , main = "Density of raw expression values for multiple arrays")

## Warning in plotDensities.EListRaw(raw_data, log = TRUE, legend = "topright", :
## NaNs produced

• raw_data = EListRaw object containing the raw expression values and information about them
• log = Specifying the densities should be plotted on the log2 scale
• legend = Specifying where the legend should be placed on the graph
• main = title of the graph

# This code block uses the imageplot() function to generate a pseudoimage for each sample to allow the user to look for any spatial abnormalities in the expressions on the chip.
num_rows <- nrow(raw_data)

find_factors <- function(num) {
    factors_list <- list()
    for (n in 1:num) {
        if((num %% n) == 0) {
            factors_list <- c(factors_list, n)
        }
    }
    return(factors_list)
}
factors <- find_factors(num_rows)

for (sample_name in colnames(raw_data$E)) {
    if (length(raw_data$printer) != 0) {
        imageplot(raw_data$E[,sample_name], layout=raw_data$printer, zlim = c((1.25*min(raw_data$E[,sample_name])),(0.75*max(raw_data$E[,sample_name]))), legend = TRUE, main = sample_name)
        }
    else {
        if (length(factors) %% 2 == 0) {
            rows <- factors[[length(factors)/2]]
            columns <- factors[[length(factors)/2+1]]
            }
        else {
            rows <- factors[[ceiling(length(factors)/2)]]
            columns <- factors[[ceiling(length(factors)/2)]]
            }
        imageplot(raw_data$E[,sample_name], layout=list(ngrid.r = 1, ngrid.c = 1, nspot.r = rows , nspot.c = columns), zlim = c((1.25*min(raw_data$E[,sample_name])),(0.75*max(raw_data$E[,sample_name]))), legend = TRUE, main = sample_name)
    }
} 

• raw_data$E[, sample_name] = specifying that we want to use the raw expression values (rows) from each sample (column) as the data for a plot
• layout = using the values from the printer variable from raw_data to specify the dimensions of the pseudoimage generated
• zlim = altering the range of intensities that will be plotted (min = 1.25 min raw expression value, max = 0.75 max raw expression value)
• legend = displays the range of the z values (raw expresison values)

# This code block uses the plotMA() function from limma to generate a MA plot for each of the samples. 
count = 0
for (sample_name in colnames(raw_data$E)) {
    count = count + 1
    suppressWarnings(limma::plotMA(raw_data,array=count,xlab="Average log-expression",ylab="Expression log-ratio(this sample vs. others)", main = sample_name))
}

• raw_data = EListRaw object containing the raw expression values and information about them
• array = choosing which array (sample) to use the data from
• xlab = label for the x axis
• ylab = label for the y axis
• main = title for the plot

# This code block uses the plotFB() function from limma to generate a FB plot to compare the foreground and background intensities in each sample. 
count = 0
for (sample_name in colnames(raw_data$E)) {
    count = count + 1
    plotFB(raw_data, array = count, xlab = "log2 Background", ylab = "log2 Foreground", main = sample_name) 
}

• raw_data = EListRaw object containing the raw expression values and information about them
• array = specifying which array (sample) to use as the data
• xlab = label for the x axis
• ylab = label for the y axis
• main = title for the plot

Normalization of the Data

After making sure that the raw data looks good, I had to remove the technical variation in the raw intensities that comes from external sources (i.e. Sample preparation) by performing normalization on the raw data. For this step, I was able to use a couple other functions from Limma called backgroundCorrect() to subtract the foreground intensities from the background intensities and normalizeBetweenArrays() to normalize the data.

corrected_data <- limma::backgroundCorrect(raw_data, method = "normexp") # Corrects intensities for background

## Array 1 corrected
## Array 2 corrected
## Array 3 corrected
## Array 4 corrected
## Array 5 corrected
## Array 6 corrected
## Array 7 corrected
## Array 8 corrected
## Array 9 corrected
## Array 10 corrected
## Array 11 corrected
## Array 12 corrected
## Array 13 corrected
## Array 14 corrected
## Array 15 corrected
## Array 16 corrected
## Array 17 corrected
## Array 18 corrected
## Array 19 corrected
## Array 20 corrected
## Array 21 corrected
## Array 22 corrected
## Array 23 corrected
## Array 24 corrected
## Array 25 corrected
## Array 26 corrected
## Array 27 corrected
## Array 28 corrected
## Array 29 corrected
## Array 30 corrected
## Array 31 corrected
## Array 32 corrected
## Array 33 corrected
## Array 34 corrected
## Array 35 corrected
## Array 36 corrected
## Array 37 corrected
## Array 38 corrected
## Array 39 corrected
## Array 40 corrected
## Array 41 corrected
## Array 42 corrected
## Array 43 corrected
## Array 44 corrected
## Array 45 corrected
## Array 46 corrected
## Array 47 corrected
## Array 48 corrected
## Array 49 corrected
## Array 50 corrected
## Array 51 corrected
## Array 52 corrected
## Array 53 corrected
## Array 54 corrected
## Array 55 corrected
## Array 56 corrected
## Array 57 corrected
## Array 58 corrected
## Array 59 corrected
## Array 60 corrected

# Input: Background corrected EListRaw, Output: Elist
norm_data <- normalizeBetweenArrays(corrected_data, method = "quantile") # Normalizes the data

• raw_data = EListRaw object containing the raw intensities and information about the arrays
• method = specifying which background correction method should be used on the raw data
• corrected_data = Background corrected EListRaw object
• method specifying which normalization method should be done on the data

QA of the Normalized Data

plotDensities(norm_data, log = TRUE, legend = "topright" , main = "Density of raw intensities for multiple arrays")

• norm_data = EList object containing normalized intensities and information about the arrays
• log = Specifying the densities should be plotted on the log2 scale
• legend = Specifying where the legend should be placed on the graph
• main = title of the graph

count = 0 
for (sample_name in colnames(raw_data$E)) {
    count = count + 1
    limma::plotMA(norm_data, array = count, xlab = "Average Log-expression", ylab = "Expression Log-ratio (this sample vs. others)", main = sample_name)
}

• norm_data = EList object containing normalized intensities and information about the arrays
• array = choosing which array (sample) to use the data from
• xlab = label for the x axis
• ylab = label for the y axis
• main = title for the plot

boxplot(log2(raw_data$E)) # Comparing the raw data to the normalized data

suppressWarnings(boxplot(log2(norm_data$E)))

• raw_data$E = Raw intensities
• norm_data$E = Normalized intensities

Generating Probe and Gene Level Data

# Generating Raw probe level data
raw_intensities_df <- as.data.frame(raw_data$E, row.names = raw_data$genes$GeneName, col.names = colnames(raw_data$E))# Generates a dataframe containing the raw intensities with the genes as the rows and samples as column names.
raw_probe_level_data_df <- cbind(raw_data$genes, raw_intensities_df)
write.csv(raw_probe_level_data_df, file = file.path(dir, "/output_data/GLDS-174_output_data/Raw_probe_level_data-GLDS-174.csv"), row.names = FALSE)

• raw_intensities_df = Variable containing the raw intensities (raw_data$E)
• row.names = Making the row names the names of the genes
• col.names = Making the column names the sample names
• raw_probe_level_data_df = Dataframe containing the raw intensities with information about each gene
• file = Choosing the output location of the csv file and the name for the file
• row.names = Telling R whether or not to export the row names into the csv file

# Generating raw gene level data
first_sample_mean = paste0(colnames(raw_data$E)[1], "_mean")
sample_one_raw = colnames(raw_data$E)[1]
summarized_columns_raw <- raw_probe_level_data_df %>% group_by(GeneName) %>% summarize(!!first_sample_mean := mean(get(sample_one_raw))) # Using this data frame to merge with
for (sample_name_raw in colnames(raw_data$E)) {
    print(sample_name_raw)
    sample_name_mean = paste0(sample_name_raw,"_mean")
    sample_name_sd = paste0(sample_name_raw,"_sd")
    sample_name_median = paste0(sample_name_raw,"_median")
    summarized_columns_raw <- merge(summarized_columns_raw, raw_probe_level_data_df %>% group_by(GeneName) %>% summarize(!!sample_name_mean := mean(get(sample_name_raw)), !!sample_name_sd := sd(get(sample_name_raw)), !!sample_name_median := median(get(sample_name_raw)), .groups = "keep"))
    } # This is where I'm merging the other data frames to the first one, to generate 1 data frame with all of the columnes (Gene_name, sample_mean, sample_sd, sample_median)

## [1] "GSM1930654_1p1"
## [1] "GSM1930655_1p2"
## [1] "GSM1930656_1p3"
## [1] "GSM1930657_1p4"
## [1] "GSM1930658_1p5"
## [1] "GSM1930659_1p6"
## [1] "GSM1930660_3p1"
## [1] "GSM1930661_3p2"
## [1] "GSM1930662_3p3"
## [1] "GSM1930663_3p4"
## [1] "GSM1930664_3p5"
## [1] "GSM1930665_3p6"
## [1] "GSM1930666_4p1"
## [1] "GSM1930667_4p2"
## [1] "GSM1930668_4p3"
## [1] "GSM1930669_4p4"
## [1] "GSM1930670_4p5"
## [1] "GSM1930671_4p6"
## [1] "GSM1930672_6p1"
## [1] "GSM1930673_6p2"
## [1] "GSM1930674_6p3"
## [1] "GSM1930675_6p4"
## [1] "GSM1930676_6p5"
## [1] "GSM1930677_6p6"
## [1] "GSM1930678_7p1"
## [1] "GSM1930679_7p2"
## [1] "GSM1930680_7p3"
## [1] "GSM1930681_7p4"
## [1] "GSM1930682_7p5"
## [1] "GSM1930683_7p6"
## [1] "GSM1930684_8p1"
## [1] "GSM1930685_8p2"
## [1] "GSM1930686_8p3"
## [1] "GSM1930687_8p4"
## [1] "GSM1930688_8p5"
## [1] "GSM1930689_8p6"
## [1] "GSM1930690_9p1"
## [1] "GSM1930691_9p2"
## [1] "GSM1930692_9p3"
## [1] "GSM1930693_9p4"
## [1] "GSM1930694_9p5"
## [1] "GSM1930695_9p6"
## [1] "GSM1930696_10p1"
## [1] "GSM1930697_10p2"
## [1] "GSM1930698_10p3"
## [1] "GSM1930699_10p4"
## [1] "GSM1930700_10p5"
## [1] "GSM1930701_10p6"
## [1] "GSM1930702_11p1"
## [1] "GSM1930703_11p2"
## [1] "GSM1930704_11p3"
## [1] "GSM1930705_11p4"
## [1] "GSM1930706_11p5"
## [1] "GSM1930707_11p6"
## [1] "GSM1930708_12p1"
## [1] "GSM1930709_12p2"
## [1] "GSM1930710_12p3"
## [1] "GSM1930711_12p4"
## [1] "GSM1930712_12p5"
## [1] "GSM1930713_12p6"

write.csv(summarized_columns_raw, file = file.path(dir, paste0("/output_data/GLDS-174_output_data/Raw_gene_level_data-GLDS-174.csv")), row.names = FALSE)

• first_sample_raw_mean = Variable containing the name of the first sample followed by _mean. Used to name the column
• sample_one_raw = Variable containing the name of the first sample
• summarized_columns_raw = Dataframe that will contain all of the summarized gene level information (GeneName, mean, SD, median)
• sample_name_raw = Variable that contains the current sample
• sample_name_mean - Variable that holds the current sample name followed by _mean. Used to name the column.
• sample_name_sd - Variable that holds the current sample name followed by _sd. Used to name the column.
• sample_name_median - Variable that holds the current sample name followed by _median. Used to name the column.
• .groups = Tells the function what to do about the grouping structure from the dataframe that it is getting the information from

# Generating Normalized probe level data
norm_intensities_df <- as.data.frame(norm_data$E, row.names = norm_data$genes$GeneName, col.names = colnames(norm_data$E))
norm_probe_level_data_df <- cbind(norm_data$genes, norm_intensities_df)
write.csv(norm_probe_level_data_df, file = file.path(dir, "/output_data/GLDS-174_output_data/Normalized_probe_level_data-GLDS-174.csv"), row.names = FALSE)

• norm_intensities_df = Variable containing the normalized intensities (norm_data$E)
• row.names = Making the row names the names of the genes
• col.names = Making the column names the sample names
• norm_probe_level_data_df = Dataframe containing the normalized intensities with information about each gene
• file = Choosing the output location of the csv file and the name for the file
• row.names = Telling R whether or not to export the row names into the csv file

# Generating normalized gene level data
first_sample_norm = paste0(colnames(norm_data$E)[1], "_mean")
sample_one_norm = colnames(norm_data$E)[1]
summarized_columns_norm <- norm_probe_level_data_df %>% group_by(GeneName) %>% summarize(!!first_sample_norm := mean(get(sample_one_norm)))
for (sample_name_norm in colnames(norm_data$E)) {
    print(sample_name_norm)
    sample_name_mean = paste0(sample_name_norm, "_mean")
    sample_name_sd = paste0(sample_name_norm, "_sd")
    sample_name_median = paste0(sample_name_norm, "_median")
    summarized_columns_norm <- merge(summarized_columns_norm, norm_probe_level_data_df %>% group_by(GeneName) %>% summarize(!!sample_name_mean := mean(get(sample_name_norm)), !!sample_name_sd := sd(get(sample_name_norm)), !!sample_name_median := median(get(sample_name_norm)), .groups = "keep"))
    }

## [1] "GSM1930654_1p1"
## [1] "GSM1930655_1p2"
## [1] "GSM1930656_1p3"
## [1] "GSM1930657_1p4"
## [1] "GSM1930658_1p5"
## [1] "GSM1930659_1p6"
## [1] "GSM1930660_3p1"
## [1] "GSM1930661_3p2"
## [1] "GSM1930662_3p3"
## [1] "GSM1930663_3p4"
## [1] "GSM1930664_3p5"
## [1] "GSM1930665_3p6"
## [1] "GSM1930666_4p1"
## [1] "GSM1930667_4p2"
## [1] "GSM1930668_4p3"
## [1] "GSM1930669_4p4"
## [1] "GSM1930670_4p5"
## [1] "GSM1930671_4p6"
## [1] "GSM1930672_6p1"
## [1] "GSM1930673_6p2"
## [1] "GSM1930674_6p3"
## [1] "GSM1930675_6p4"
## [1] "GSM1930676_6p5"
## [1] "GSM1930677_6p6"
## [1] "GSM1930678_7p1"
## [1] "GSM1930679_7p2"
## [1] "GSM1930680_7p3"
## [1] "GSM1930681_7p4"
## [1] "GSM1930682_7p5"
## [1] "GSM1930683_7p6"
## [1] "GSM1930684_8p1"
## [1] "GSM1930685_8p2"
## [1] "GSM1930686_8p3"
## [1] "GSM1930687_8p4"
## [1] "GSM1930688_8p5"
## [1] "GSM1930689_8p6"
## [1] "GSM1930690_9p1"
## [1] "GSM1930691_9p2"
## [1] "GSM1930692_9p3"
## [1] "GSM1930693_9p4"
## [1] "GSM1930694_9p5"
## [1] "GSM1930695_9p6"
## [1] "GSM1930696_10p1"
## [1] "GSM1930697_10p2"
## [1] "GSM1930698_10p3"
## [1] "GSM1930699_10p4"
## [1] "GSM1930700_10p5"
## [1] "GSM1930701_10p6"
## [1] "GSM1930702_11p1"
## [1] "GSM1930703_11p2"
## [1] "GSM1930704_11p3"
## [1] "GSM1930705_11p4"
## [1] "GSM1930706_11p5"
## [1] "GSM1930707_11p6"
## [1] "GSM1930708_12p1"
## [1] "GSM1930709_12p2"
## [1] "GSM1930710_12p3"
## [1] "GSM1930711_12p4"
## [1] "GSM1930712_12p5"
## [1] "GSM1930713_12p6"

write.csv(summarized_columns_norm, file = file.path(dir, "/output_data/GLDS-174_output_data/Normalized_gene_level_data-GLDS-174.csv"), row.names = FALSE)

• first_sample_norm_mean = Variable containing the name of the first sample followed by _mean. Used to name the column
• sample_one_norm = Variable containing the name of the first sample
• summarized_columns_norm = Dataframe that will contain all of the summarized gene level information (GeneName, mean, SD, median)
• sample_name_norm = Variable that contains the current sample
• sample_name_mean - Variable that holds the current sample name followed by _mean. Used to name the column.
• sample_name_sd - Variable that holds the current sample name followed by _sd. Used to name the column.
• sample_name_median - Variable that holds the current sample name followed by _median. Used to name the column.
• .groups = Tells the function what to do about the grouping structure from the dataframe that it is getting the information from

Differential Gene Analysis

Adding Gene Annotations